In this paper, a new algorithm for the total transfer capability (TTC) calculation is proposed The algorithm is based on full ac optimal power flow (OPF) solution to account for the effects of active and reactive powe...
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ISBN:
(纸本)0780385608
In this paper, a new algorithm for the total transfer capability (TTC) calculation is proposed The algorithm is based on full ac optimal power flow (OPF) solution to account for the effects of active and reactive power flows, voltage limits, and line flow limits. evolutionary programming (EP) is used to solve the OPF-TTC problem. The realpower output of generators in source area, real and reactive load in sink area, and bus voltage of generators can be adjusted to obtain the maximum transfer capability. The proposed method is tested on the IEEE 30-bus system and the results are compared favorably with that from the continuation power flow (CPF) method.
Reactive power loading has been known to be one of the factors, which caused a power system to be in a stressed condition. As a result, system may be operating close to its voltage stability limit leading to voltage d...
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ISBN:
(纸本)0889863954
Reactive power loading has been known to be one of the factors, which caused a power system to be in a stressed condition. As a result, system may be operating close to its voltage stability limit leading to voltage decay at a particular load bus. Hence, some measures should be taken in order to support for the reactive power loading and thus improves the voltage stability condition of the system. This paper presents an optimal transformer tap changer setting based on evolutionary programming (EP) optimization technique for voltage stability improvement in power system. The objective function is to increase the voltage stability condition utilizing a line-based index as the fitness function. Improvement of voltage stability condition is indicated by the reduction in the values of this index. Results obtained from the test implied the new transformer tap changer setting values for voltage stability improvement in the system. It was revealed that the use of EP optimizing technique for improving the voltage stability condition using the line index as the fitness is simple, fast, accurate and reliable;indicating it as a feasible technique for further reactive power planning scheme. The proposed optimization technique was validated on the IEEE Reliability Test System (IEEERTS) and results are included to realize the effectiveness of the proposed EP optimization technique.
The paper proposes an application of Hybrid evolutionary programming to Reactive Power Planning. Reactive power planning is a non-smooth and non-differentiable optimization problem for a multi-objective function. The ...
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ISBN:
(纸本)078038718X
The paper proposes an application of Hybrid evolutionary programming to Reactive Power Planning. Reactive power planning is a non-smooth and non-differentiable optimization problem for a multi-objective function. The objective functions deals with the minimization of operating cost by reducing real power loss, improving the voltage profile and minimizing the allocation cost of additional reactive power sources. The proposed method is developed in such a way that a standard evolutionary programming (EP) method is acting as a base level search, which makes a quick decision to direct the search towards the optimal region. and local optimization by direct search and systematic reduction in size of search region method is next employed to do fine tuning. The Reactive Power Planning using Hybrid evolutionary programming is demonstrated with the IEEE 30-bus system. The comprehensive simulation results show that Hybrid evolutionary programming is a suitable method to solve the Reactive power-planning problem.
This paper develops a parallel evolutionary programming based optimal power flow solution algorithm. The proposed approach is less sensitive to the choice of starting points and types of generator cost curves. To impr...
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ISBN:
(纸本)0780384032
This paper develops a parallel evolutionary programming based optimal power flow solution algorithm. The proposed approach is less sensitive to the choice of starting points and types of generator cost curves. To improve the robustness and speed of convergence of the algorithm, population and gradient acceleration techniques are incorporated. The developed algorithm is implemented on a thirty-six-processor Beowulf cluster. The proposed approach has been tested on the IEEE 118-bus system under master-slave, dual-direction ring and 2D-mesh topologies. Computational speedup and generation costs for each parallel topology with different number of processors are then compared to those of the sequential EP approach.
Genetic algorithm and evolutionary programming are two generally used evolutionary algorithms. Due to the difference of their origin, there are a lot of differences between their biologic bases, algorithm operation an...
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ISBN:
(纸本)0780384032
Genetic algorithm and evolutionary programming are two generally used evolutionary algorithms. Due to the difference of their origin, there are a lot of differences between their biologic bases, algorithm operation and some other operational details. So, the performances of the two algorithms are different. In this paper, these differences are analyzed comprehensively by theory and revealed by simulation experiments. The results show that the performance of evolutionary programming is better than that of genetic algorithm and the evolutionary programming is more suitable for practical applications.
This paper addresses the optimization of voltage control in distribution systems in the presence of distributed generation. The objective is minimizing the voltage deviations at the load nodes with respect to specifie...
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ISBN:
(纸本)0780382714
This paper addresses the optimization of voltage control in distribution systems in the presence of distributed generation. The objective is minimizing the voltage deviations at the load nodes with respect to specified reference values. The optimization is solved by means of a new approach based on nested evolutionary programming. Results are shown on a test system including controls at the HV/MV and MV/LV substations, and voltage-controlled local generation sources.
evolutionary programming is a good global optimization method. By introducing the improved adaptive mutation operation and improved selection operation based on thickness adjustment of artificial immune system into tr...
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ISBN:
(纸本)0780384032
evolutionary programming is a good global optimization method. By introducing the improved adaptive mutation operation and improved selection operation based on thickness adjustment of artificial immune system into traditional evolutionary programming, a fast immunized evolutionary programming is proposed in this paper. At last, this algorithm is verified by simulation experiment of typical optimization function. The results of the experiment show that, the proposed fast immunized evolutionary programming can improve not only the convergent speed of original algorithm but also the computation effect of original algorithm, and is a very good optimization method.
This paper proposes a hybrid method that integrates the main features of particle swarm optimization (PSO) and evolutionary programming (EP) for solution of non-convex economic load dispatch (ELD) problems having non-...
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ISBN:
(纸本)078038718X
This paper proposes a hybrid method that integrates the main features of particle swarm optimization (PSO) and evolutionary programming (EP) for solution of non-convex economic load dispatch (ELD) problems having non-linearities like valve point loadings. Algorithms based on PSO, evolutionary programming (EP) and PSO embedded EP techniques have been developed and tested on a practical non-convex ELD problem with valve point loading effects considered in the cost functions. Numerical results show that all the algorithms are capable of finding feasible near global solutions within a reasonable time but PSO embedded EP-algorithm with Gaussian mutation appears to outperform the other two in terms of convergence speed, solution time and quality of solution.
The LMS algorithm is commonly used in the optimum design of the adaptive filter. Because the LMS adaptive algorithm is a simple algorithm and it can be realized easily. But the convergence behavior and maladjustment o...
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ISBN:
(纸本)0780386477
The LMS algorithm is commonly used in the optimum design of the adaptive filter. Because the LMS adaptive algorithm is a simple algorithm and it can be realized easily. But the convergence behavior and maladjustment of the LMS algorithm is seriously affected by the step-size, and the optimum parameter of step-size can't be calculated easily. evolutionary programming is an optimum algorithm in which the optimization of N-dimensions real-numbers are research objects. In this paper, FIR Filter is an example. In the design of the Adaptive Filter, we use a fast evolutionary programming Algorithm. Cauchy mutation takes the place of Gauss mutation for improving the speed of the convergence. This algorithm is not depend on any parameter;W can get a good result by the simulation and indicate the validity of the algorithm.
Optimal trajectory planning for robot manipulators is always the hot spot in the research field of robotics. The performance indexes used in optimal trajectory planning are classified into two main categories. One is ...
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ISBN:
(纸本)0780386418
Optimal trajectory planning for robot manipulators is always the hot spot in the research field of robotics. The performance indexes used in optimal trajectory planning are classified into two main categories. One is called optimum traveling time. The other is called optimum mechanical energy of the actuators. The current trajectory planning algorithms are designed based on one of the above two performance indexes. Unfortunately, there are few planning algorithms designed to satisfy two performance indexes simultaneously. There are some deficiencies appeared in the existing integrated optimization algorithms of trajectory planning. In order to overcome those deficiencies, the integrated optimization algorithms of trajectory planning are presented based on the complete analysis for trajectory planning of robot manipulators.
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